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نتیجه جستجو - Similarity measures

تعداد مقالات یافته شده: 16
ردیف عنوان نوع
1 A hybrid deep learning image-based analysis for effective malware detection
یک تجزیه و تحلیل مبتنی بر یادگیری عمیق ترکیبی برای تشخیص مؤثر بدافزار -2019
The explosive growth of Internet and the recent increasing trends in automation using intelligent appli- cations have provided a veritable playground for malicious software (malware) attackers. With a variety of devices connected seamlessly via the Internet and large amounts of data collected, the escalating mal- ware attacks and security risks are a big concern. While a number of malware detection methods are available, new methods are required to match with the scale and complexity of such a data-intensive environment. We propose a novel and unified hybrid deep learning and visualization approach for an effective detection of malware. The aim of the paper is two-fold: 1. to present the use of image-based techniques for detecting suspicious behavior of systems, and 2. to propose and investigate the application of hybrid image-based approaches with deep learning architectures for an effective malware classification. The performance is measured by employing various similarity measures of malware behavior patterns as well as cost-sensitive deep learning architectures. The scalability is benchmarked by testing our proposed hybrid approach with both public and privately collected large malware datasets that show high accuracy of our malware classifiers.
Keywords: Malware detection | Similarity mining | Image analysis | Evaluation metrics | Machine learning | Deep learning architectures
مقاله انگلیسی
2 A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems
اندازه گیری شباهت sub-one مبتنی بر شبه هنجار برای فیلتر مشترک در سیستم های توصیه گر-2019
Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhoodbased CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.
Keywords: Recommender system | Collaborative filtering | Neighborhood-based CF | Similarity measure | p quasi-norm
مقاله انگلیسی
3 New bounded variation based similarity measures between Atanassov intuitionistic fuzzy sets for clustering and pattern recognition
اقدامات شباهت مبتنی بر تغییرات محدود محدود بین مجموعه های فازی شهودی Atanassov برای خوشه بندی و تشخیص الگو-2019
The distance and similarity measures are two interrelated depictions of the patterns which signify the categorization between the Atanassov intuitionistic fuzzy sets (AIFSs) by evaluating the degree of belongingness. In this work, we propose a new similarity measure, termed as hybrid similarity measure, by the combination of intuitionistic fuzzy bounded variation (IFBV) and intuitionistic fuzzy metric based measures. The concept of IFBV which is a technique to approximate arc length of an intuitionistic fuzzy-valued function (IFVF) is also introduced here. The IFVF over an AIFS is geometrically evolved through the generalization of the p-summable IFBV, that is, connecting all the elements of AIFS lie on the structure of IFBV corresponding to power p. The proposed measure overcomes the shortcomings of intuitionistic fuzzy metric based similarity measures by incorporating more flexibility into it. The hybrid similarity measure has been successfully implemented and applied on the several real-world applications in the field of pattern recognition as well as clustering. Further, a detailed comparison of results has been shown against the other existing similarity measures to demonstrate the superiority and validity of the proposed hybrid similarity measure.
Keywords: Intuitionistic fuzzy set (IFS) | Similarity measure | Bounded variation | Pattern recognition | Clustering analysis
مقاله انگلیسی
4 A new similarity measure for collaborative filtering based recommender systems
یک اندازه گیری شباهت جدید برای سیستم های توصیه گر مبتنی بر فیلتر مشترک-2019
The objective of a recommender system is to provide customers with personalized recommendations while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items. Several similarity functions have been proposed, with different performances in terms of accuracy and quality of recommendations. In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is determined through the following paper contributions: 1) transforming some intuitive and qualitative conditions, that should be satisfied by the similarity measure, into relevant mathematical equations namely: the integral equation, the linear system of differential equations and a non-linear system and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive experimental study driven on a benchmark datasets shows that the proposed similarity measure is very competitive, especially in terms of accuracy, with regards to some representative similarity measures of the literature.
Keywords: Recommendation systems | Collaborative filtering | Neighborhood based CF | Similarity measure
مقاله انگلیسی
5 An intuitionistic fuzzy set based hybrid similarity model for recommender system
مدل شباهت ترکیبی مبتنی بر مجموعه فازی شهودی برای سیستم توصیه گر-2019
In general, a practical online recommendation system does not rely on only one algorithm but adopts dif- ferent types of algorithms to predict user preferences. Although most of similarity measures can rapidly calculate the similarity on the basis of co-rated items, their prediction accuracy is not satisfactory in the case of sparse datasets. Making full use of all the rating information can effectively im prove the rec- ommendation quality, but it reduces the system efficiency because all the ratings need to be calculated. To recommend items for target users rapidly and accurately, this paper designs a hybrid item similarity model that achieves a trade-offbetween prediction accuracy and efficiency by combining the advantages of the two above-mentioned methods. First, we introduce an adjusted Google similarity to rapidly and precisely calculate the item similarity in the condition of enough co-rated items. Subsequently, an intu- itionistic fuzzy set (IFS) based Kullback–Leibler (KL) similarity is presented from the perspective of user preference probability to effectively compute the item similarity in the condition of rare co-rated items. Finally, the two proposed schemes are integrated by an adjusted variable to comprehensively evaluate the similarity values when the number of co-rated items lies in a certain range of value. The proposed model is implemented and tested on some benchmark datasets with different thresholds of co-rated items. The experimental results indication that the proposed system has a favorable efficiency and guarantees the quality of recommendations
Keywords: Recommender system | Collaborative filtering | Normalized Google distance | Intuitionistic fuzzy set | Kullback–Leibler divergence
مقاله انگلیسی
6 Effective aggregation of various summarization techniques
متراکم سازی موثر روشهای مختلف خلاصه سازی-2018
A large number of extractive summarization techniques have been developed in the past decade, but very few enquiries have been made as to how these differ from each other or what are the factors that actually affect these systems. Such meaningful comparison if available can be used to create a robust ensemble of these approaches, which has the possibility to consistently outperform each individual summarization system. In this work we examine the roles of three principle components of an extractive summarization technique: sentence ranking algorithm, sentence similarity metric and text representation scheme. We show that using a combination of several different sentence similarity measures, rather than only one, significantly improves performance of the resultant meta-system. Even simple ensemble techniques, when used in an informed manner, prove to be very effective in improving the overall performance and consistency of summarization systems. A statistically significant improvement of about 5% to 10% in ROUGE-1 recall was achieved by aggregating various sentence similarity measures. As opposed to this aggregation of several ranking algorithms did not show a significant improvement in ROUGE score, but even in this case the resultant meta-systems were more robust than candidate systems. The results suggest that new extractive summarization techniques should particularly focus on defining a better sentence similarity metric and use multiple sentence similarity scores and ranking algorithms in favour of a particular combination.
keywords: Summarization |Ensemble
مقاله انگلیسی
7 Identifying similar days for air traffic management
شناسایی روزهای مشابه برای مدیریت ترافیک هوایی-2017
Air traffic managers face challenging decisions due to uncertainity in weather and air traffic. One way to support their decisions is to identify similar historical days, the traffic management actions taken on those days, and the resulting outcomes. We develop similarity measures based on quarter-hourly ca pacity and demand data at four case study airportsdEWR, SFO, ORD and JFK. We find that dimensionality reduction is feasible for capacity data, and base similarity on principal components. Dimensionality reduction cannot be efficiently performed on demand data, consequently similarity is based on original data. We find that both capacity and demand data lack natural clusters and propose a continuous similarity measure. Finally, we estimate overall capacity and demand similarities, which are visualized using Metric Multidimensional Scaling plots. We observe that most days with air traffic management activity are similar to certain other days, validating the potential of this approach for decision support.
Keywords: Similar days | Clustering capacity and demand data | Decision support | Air traffic management
مقاله انگلیسی
8 Impact of a priori MS/MS intensity distributions on database search for peptide identification
تأثیر توزیع شدت اولیه MS / MS در جستجوی پایگاه داده برای شناسایی پپتید-2017
Many database search methods have been developed for peptide identification throughout a large peptide data set. Most of these approaches attempt to build a decision function that allows the identification of an experimental spectrum. This function is built either starting from similarity measures for the database peptides to identify the most similar one to a given spectrum, or by applying useful learning techniques considering the database itself as a training data. In this paper, we propose a peptide identification method based on a similarity measure for peptide-spectrum matches. Our method takes into account peak intensity distribution and applies it in a probabilistic scoring model to rank peptide matches. The main goal of our approach is to highlight the relationship between peak intensities and peptide cleavage positions on the one hand and to show its impact on peptide identification on the other hand. To evaluate our method, a set of experiments have been undertaken into two high mass spectrum accuracy data sets. The obtained results show the effectiveness of our proposed approach.
Keywords: Mass spectrometry | Tandem mass spectrum | Peptide identification | Database search | De novo identification | Collision-induced dissociation
مقاله انگلیسی
9 A generic trajectory similarity operator in moving object databases
یک اپراتور شباهت مسیری عمومی در پایگاه داده های حرکتی شی-2017
Evaluating similarity between trajectories of moving objects is important for wide range of applications. The existing similarity measures typically define some meaning of similarity and propose algorithms for computing it. We think that the meaning of similarity is application dependant, and should only be determined by the user. Therefore, there is a need for a generic approach where users can define the meaning of similarity. In this paper, we propose a parametrized similarity operator, based on the time warped edit distance, where the meaning of similarity is generic and left for user to define. Our proposed operator is implemented in SECONDO and evaluated using both synthetic and real datasets. The results were promising and as expected.
KEYWORDS : Trajectory similarity | Moving objects databases | SECONDO | TWED
مقاله انگلیسی
10 Power series models of self-similarity in social networks
مدل های سری قدرت از خود خواهی در شبکه های اجتماعی-2017
The evolution of a social network is associated with replicating self-similarity at many lev els, the nature of interconnections can serve as a measure of the optimality of its organi zation. Closeness to self-similarity in the interconnections is proposed as a measure of the optimality of the organization. Two power series models are proposed to represent self similarity and they are compared to the Zipf and Benford distributions. In contrast with the Zipf distribution where the middle term is the harmonic mean of the adjoining terms, our distribution considers the middle term to be the geometric mean. In one of the power series models, the scaling factor at one level is shown to be the golden ratio. A model for evolution of networks by oscillations between two different self-similarity measures is described.
Keywords: Social networks | Self-similarity | 80–20 phenomenon | Connectivity | Golden ratio
مقاله انگلیسی
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